The existing privacy-preserving machine learning (PPML) works focus exclusively on training (generalized) linear models. They rely on centralized solutions where the learning task is securely outsourced to a server, notably using homomorphic encryption (HE) techniques. As such, these works do not solve the problem of privacy-preserving distributed ML, where multiple parties collaboratively train an ML model on their data. To address the latter, several works propose multi-party computation (MPC) [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} solutions where several tasks, such as clustering and regression, are distributed among 2, 3, or 4 servers [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [13]}. Although such approaches, however, limit the number of parties among which the trust is split, often assume an honest majority among the computing servers, and require parties to communicate (i.e., secret share) their data outside their premises. This might not be acceptable due to the privacy and confidentiality requirements and the strict data protection regulations.
